粒子群优化算法在函数优化上的研究与发展  

Research and development of particle swarm optimization algorithm in function optimazation

作　　者：陈永刚[1] 魏汪洋[1] 肖春宝[1] 

机构地区：[1]河南科技大学电子信息工程学院,河南洛阳471003

出　　处：《西安邮电学院学报》2009年第3期113-116,共4页Journal of Xi'an Institute of Posts and Telecommunications

基　　金：河南省基础与前沿技术研究项目(编号:072300410210)

摘　　要：粒子群优化算法(PSO)与其他演化算法相似,也是基于群体的。每个粒子被随机初始化以表示一个可能的解,并在解空间通过更新迭代搜索最优解。该算法的特点是简单容易实现而又功能强大。该算法最初被提出来主要应用于函数优化。经过几年的发展,已经出现了大量的改进算法。本文总结了这些改进算法的基本主要形式,并给出了未来可能的研究方向。Particle swarm optimization（PSO）is an optimal technique based on population, which is the same to other evolutionary compution. It is initialized with a population of random solutions and searches for optima by updating generations. The characteristics of the algorithm are of simple implementation and excellent performance. Application of this algorithm is in function optimization in the early period. Lots of improved algorithms are presented after several years＇ development. This paper summarizes the basic and main form of these improved algorithms and gives the future research directions.

关 键 词：粒子群算法 函数优化 群智能 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]